On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. annual mortality rate, along with higher-frequency regressors, e.g. daily temperature and precipitation. Practitioners use summaries of the higher-frequency regressors in fixed effects panel models. The choice over summary statistics amounts to model selection. Some practitioners use Monte Carlo cross-validation (MCCV) to justify a particular specification. However, conventional implementation of MCCV with fixed testing-to-full sample ratios tends to select over-fit models. This paper presents conditions under which MCCV, and also information criteria, can deliver consistent model selection. Previous work has established that the Bayesian information criterion (BIC) can be inconsistent for non-nested selection. We illustrate that the BIC can also be inconsistent in our framework, when all candidate models are misspecified. Our results have practical implications for empirical conventions in climate change impact studies. Specifically, they highlight the importance of a priori information provided by the scientific literature to guide the models considered for selection. We emphasize caution in interpreting model selection results in settings where the scientific literature does not specify the relationship between the outcome and the weather variables.Comment: Additional simulation results available from authors by reques

Predictive Modeling in Higher Education: Determining Factors of Academic Performance

For several decades in the field of data mining in education (EDM), predictive learning has remained one of the most popular and internationally discussed research topics. Specifically, data mining is used to predict educational outcomes such as academic performance, retention, success, satisfaction, achievement and dropout rates. In the management practice of higher education institutions, on the basis of an operational forecast, measures are developed and implemented to support those students who fall into the risk group.Our study is aimed at substantiating a model for predicting the early departure of students using an artificial neural network and analyzing predictors that increase the accuracy of predicting successful graduation from a Russian university. This work will expand the international practice of comparative research in higher education.The paper confirms the already existing hypotheses about the influence of a number of factors on the prediction of academic performance and suggests the need to test their universality or specificity in a particular institution of higher education. We also proved that an artificial neural network model with a certain set of attributes can be applied in the context of a single higher education institution, regardless of specialization. To determine the potential risk group of students, a binary classification prediction model is used. The overall prediction accuracy of a neural network with combined data reaches 88%. For this neural network model, the basic predictors that affect the accuracy of the forecast are the cumulative average level of achievement (CGPA) and the year of admission to the university

Measurement of the Positive Muon Lifetime and Determination of the Fermi Constant to Part-per-Million Precision

We report a measurement of the positive muon lifetime to a precision of 1.0 parts per million (ppm); it is the most precise particle lifetime ever measured. The experiment used a time-structured, low-energy muon beam and a segmented plastic scintillator array to record more than 2 x 10^{12} decays. Two different stopping target configurations were employed in independent data-taking periods. The combined results give tau_{mu^+}(MuLan) = 2196980.3(2.2) ps, more than 15 times as precise as any previous experiment. The muon lifetime gives the most precise value for the Fermi constant: G_F(MuLan) = 1.1663788 (7) x 10^-5 GeV^-2 (0.6 ppm). It is also used to extract the mu^-p singlet capture rate, which determines the proton's weak induced pseudoscalar coupling g_P.Comment: Accepted for publication in Phys. Rev. Let

Improved Measurement of the Positive Muon Lifetime and Determination of the Fermi Constant

The mean life of the positive muon has been measured to a precision of 11 ppm using a low-energy, pulsed muon beam stopped in a ferromagnetic target, which was surrounded by a scintillator detector array. The result, tau_mu = 2.197013(24) us, is in excellent agreement with the previous world average. The new world average tau_mu = 2.197019(21) us determines the Fermi constant G_F = 1.166371(6) x 10^-5 GeV^-2 (5 ppm). Additionally, the precision measurement of the positive muon lifetime is needed to determine the nucleon pseudoscalar coupling g_P.Comment: As published version (PRL, July 2007

The simulation of the activity dependent neural network growth

It is currently accepted that cortical maps are dynamic constructions that are altered in response to external input. Experience-dependent structural changes in cortical microcurcuts lead to changes of activity, i.e. to changes in information encoded. Specific patterns of external stimulation can lead to creation of new synaptic connections between neurons. The calcium influxes controlled by neuronal activity regulate the processes of neurotrophic factors released by neurons, growth cones movement and synapse differentiation in developing neural systems. We propose a model for description and investigation of the activity dependent development of neural networks. The dynamics of the network parameters (activity, diffusion of axon guidance chemicals, growth cone position) is described by a closed set of differential equations. The model presented here describes the development of neural networks under the assumption of activity dependent axon guidance molecules. Numerical simulation shows that morpholess neurons compromise the development of cortical connectivity.Comment: 10 pages, 2 figure

Baikal-GVD: status and prospects

Baikal-GVD is a next generation, kilometer-scale neutrino telescope under construction in Lake Baikal. It is designed to detect astrophysical neutrino fluxes at energies from a few TeV up to 100 PeV. GVD is formed by multi-megaton subarrays (clusters). The array construction started in 2015 by deployment of a reduced-size demonstration cluster named "Dubna". The first cluster in its baseline configuration was deployed in 2016, the second in 2017 and the third in 2018. The full scale GVD will be an array of ~10000 light sensors with an instrumented volume of about 2 cubic km. The first phase (GVD-1) is planned to be completed by 2020-2021. It will comprise 8 clusters with 2304 light sensors in total. We describe the design of Baikal-GVD and present selected results obtained in 2015-2017.Comment: 9 pages, 8 figures. Conference proceedings for QUARKS201